Brain activity tracking during electronic gaming

ABSTRACT

An illustrative system includes a brain interface system configured to be worn by a user and to output brain activity data representative of brain activity of the user while the user concurrently plays an electronic game and a computing device configured to obtain the brain activity data and modify, based on the brain activity data, an attribute of the electronic game.

RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/234,113, filed on Aug. 17, 2021, and to U.S. Provisional Patent Application No. 63/154,157, filed on Feb. 26, 2021, which is incorporated herein by reference in its entirety.

BACKGROUND INFORMATION

Electronic games (e.g., online games, games provided by way of electronic gaming devices, etc.) are a source of entertainment, therapy, relaxation, and even income for many users. Gaming can affect a user's brain in a variety of ways. For example, a user's brain may be fatigued, stimulated, engaged, and/or otherwise affected depending on a difficulty level of an electronic game, a content of the electronic game, an amount of time spent playing the electronic game, and/or various other aspects of the electronic game. As such, real-time insight into how an electronic game affects a user's brain while the user plays the electronic game may result in an improved gaming experience for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments and are a part of the specification. The illustrated embodiments are merely examples and do not limit the scope of the disclosure. Throughout the drawings, identical or similar reference numbers designate identical or similar elements.

FIG. 1 shows an exemplary gaming monitoring system.

FIGS. 24, 5A and 5B show various optical measurement systems that may implement the brain interface system shown in FIG. 1 .

FIGS. 6-7 show various multimodal measurement systems that may implement the brain interface system shown in FIG. 1 .

FIG. 8 shows an exemplary magnetic field measurement system that may implement the brain interface system shown in FIG. 1 .

FIG. 9 shows an exemplary configuration in which a gaming system presents an electronic game to a user and outputs game data associated with the electronic game.

FIG. 10 shows an exemplary configuration in which a sensor outputs sensor data associated with the user.

FIG. 11 shows an illustrative configuration in which a computing device is configured to implement a machine learning model to perform one or more operations based on the brain activity data output by brain interface system.

FIG. 12 shows an exemplary graphical user interface.

FIGS. 13-14 show implementations of brain activity content.

FIG. 15 illustrates an exemplary method.

FIG. 16 illustrates an exemplary computing device.

DETAILED DESCRIPTION

Tracking of a user's brain activity while the user concurrently plays an electronic game is described herein. For example, an illustrative system may include a brain interface system and a computing device. The brain interface system may be configured to be worn by a user and to output brain activity data representative of brain activity of the user while the user concurrently plays an electronic game. The computing device may be configured to obtain the brain activity data and modify, based on the brain activity data, an attribute of the electronic game.

By tracking the user's brain activity while the user concurrently plays an electronic game, various benefits may be realized for the user and/or others. For example, the tracked brain activity may be used to estimate a cognitive load of the user while the user plays an electronic game, infer a cognitive strategy used by the user while the user plays the electronic game, determine when the user should take a break from the electronic game, and/or monitor performance-related brain activity, such as learning and/or error perception. Additionally or alternatively, based on the user's brain activity, the electronic game may be adjusted (e.g., in substantially real time as the user plays the electronic game) to more effectively achieve a desired effect on the user's brain, such as by helping the user realize a desired mental state or emotional response. These and other benefits are described more fully herein.

FIG. 1 shows an exemplary gaming monitoring system 100. As shown, gaming monitoring system 100 includes a brain interface system 102 and a computing device 106.

Brain interface system 102 is configured to be worn by a user and to output brain activity data representative of brain activity of the user while the user plays an electronic game.

As used herein, an electronic game may refer to any computer-based game that may be presented to the user by way of one or more devices. For example, the electronic game may be an internet-based game (e.g., an online single-player or multi-player game), a game presented by a gaming system (e.g., a gaming console, a mobile device, a personal computer, etc.), and/or any other game as may serve a particular implementation. In some examples, computing device 106 itself may present the electronic game to the user. Alternatively, a gaming system different than computing device 106 may present the electronic game to the user. In some examples, the electronic game requires physical interaction by the user with one or more user input devices (e.g., a keyboard, a joystick, a mouse, a controller, etc.) for the user to play the electronic game.

As described herein, the brain activity data may include any data output by any of the implementations of brain interface system 102 described herein. For example, the brain activity data may include or be based on optical-based, electrical-based, and/or magnetic field-based measurements of activity within the brain, as described herein.

In some examples, the brain activity data may indicate how well the user is able to function mentally while playing the electronic game. For example, the brain activity data may indicate how well the user is able to focus on certain tasks within the electronic game, how well the user is able to exercise impulse control when playing the electronic game, what the user's mental state is while the user plays the electronic game (e.g., how stressed, anxious, sleep deprived, and/or calmness of the user), how well the user cooperates or otherwise gets along with others (e.g., other gamers) while playing the electronic game, etc. In some examples, one or more of these measures may be represented by a single brain activity score that is derived from the brain activity data. This single brain activity score may be generated in any suitable manner.

The measured brain activity could be related to physiological brain states and/or mental brain states, e.g., joy, excitement, relaxation, surprise, fear, stress, anxiety, sadness, anger, disgust, contempt, contentment, calmness, approval, focus, attention, creativity, cognitive assessment, positive or negative reflections/attitude on experiences or the use of objects, etc. Further details on the methods and systems related to a predicted brain state, behavior, preferences, or attitude of the user, and the creation, training, and use of neuromes can be found in U.S. patent application Ser. No. 17/188,298, filed Mar. 1, 2021, issued as U.S. Pat. No. 11,132,625. Exemplary measurement systems and methods using biofeedback for awareness and modulation of mental state are described in more detail in U.S. patent application Ser. No. 16/364,338, filed Mar. 26, 2019, issued as U.S. Pat. No. 11,006,876. Exemplary measurement systems and methods used for detecting and modulating the mental state of a user using entertainment selections, e.g., music, film/video, are described in more detail in U.S. patent application Ser. No. 16/835,972, filed Mar. 31, 2020, issued as U.S. Pat. No. 11,006,878. Exemplary measurement systems and methods used for detecting and modulating the mental state of a user using product formulation from, e.g., beverages, food, selective food/drink ingredients, fragrances, and assessment based on product-elicited brain state measurements are described in more detail in U.S. patent application Ser. No. 16/853,614, filed Apr. 20, 2020, issued as U.S. Pat. No. 11,172,869. Exemplary measurement systems and methods used for detecting and modulating the mental state of a user through awareness of priming effects are described in more detail in U.S. patent application Ser. No. 16/885,596, filed May 28, 2020, published as US2020/0390358A1. Exemplary measurement systems and methods used for wellness therapy, such as pain management regime, are described more fully in U.S. Provisional Application No. 63/188,783, filed May 14, 2021. These applications and corresponding U.S. patents and publications are incorporated herein by reference in their entirety.

Computing device 106 is configured to obtain (e.g., receive or otherwise access) the brain activity data and, based on the brain activity data, modify an attribute of the electronic game. To this end, computing device 106 may generate game control data based on the brain activity data. The game control data may be used to modify the attribute of the electronic game.

As used herein, an attribute of an electronic game that may be modified may include any customizable feature of the electronic game. For example, the attribute may refer to a difficulty level of the electronic game, content that is presented to the user while the user plays the electronic game, a storyline of the electronic game, a duration of the electronic game, a volume of the electronic game, a color scheme used in the electronic game, etc.

In some examples, brain interface system 102 may output the brain activity data in substantially real time while the user plays the electronic game, or concurrently while the user plays the electronic game. Furthermore, computing device 106 may obtain the brain activity data and modify the attribute of the electronic game in substantially real time while the user plays the electronic game, or concurrently while the user plays the electronic game. As used herein, “real time” and “substantially real time” and “concurrently” will be understood to relate to data processing and/or other actions that are performed immediately, as well as conditions and/or circumstances that are accounted for as they exist in the moment, or at the same time, when the processing or other actions are performed. For example, a real-time operation may refer to an operation that is performed immediately and without undue delay, even if it is not possible for there to be absolutely zero delay. Similarly, real-time data, real-time representations, real-time conditions, at the same time conditions, and so forth, will be understood to refer to data, representations, and conditions that relate to a present moment in time or a moment in time when decisions are being made and operations are being performed (e.g., even if after a short delay), such that the data, representations, conditions, and so forth are temporally relevant to the decisions being made and/or the operations being performed.

Computing device 106 may be implemented by one or more computing devices, such as one or more personal computers, mobile devices (e.g., a mobile phone, a tablet computer, etc.), servers, and/or any other type of computing device as may serve a particular implementation.

As shown, computing device 106 may include memory 108 and a processor 110. Computing device 106 may include additional or alternative components as may serve a particular implementation. Each component may be implemented by any suitable combination of hardware and/or software.

Memory 108 may maintain (e.g., store) executable data used by processor 110 to perform one or more of the operations described herein as being performed by computing device 106. For example, memory 108 may store instructions 112 that may be executed by processor 110 to generate game control data and/or perform one or more operations based on the game control data. Instructions 112 may be implemented by any suitable application, program, software, code, and/or other executable data instance. Memory 108 may also maintain any data received, generated, managed, used, and/or transmitted by processor 110.

Processor 110 may be configured to perform (e.g., execute instructions 112 stored in memory 108 to perform) various operations described herein as being performed by computing device 106. Examples of such operations are described herein.

Brain interface system 102 may be implemented by any suitable wearable non-invasive brain interface system as may serve a particular implementation. For example, brain interface system 102 may be implemented by a wearable optical measurement system configured to perform optical-based brain data acquisition operations, such as any of the wearable optical measurement systems described in U.S. patent application Ser. No. 17/176,315, filed Feb. 16, 2021 and published as US2021/0259638A1; U.S. patent application Ser. No. 17/176,309, filed Feb. 16, 2021 and published as US2021/0259614A1; U.S. patent application Ser. No. 17/176,460, filed Feb. 16, 2021 and issued as U.S. Pat. No. 11,096,620; U.S. patent application Ser. No. 17/176,470, filed Feb. 16, 2021 and published as US2021/0259619A1; U.S. patent application Ser. No. 17/176,487, filed Feb. 16, 2021 and published as US2021/0259632A1; U.S. patent application Ser. No. 17/176,539, filed Feb. 16, 2021 and published as US2021/0259620A1; U.S. patent application Ser. No. 17/176,560, filed Feb. 16, 2021 and published as US2021/0259597A1; U.S. patent application Ser. No. 17/176,466, filed Feb. 16, 2021 and published as US2021/0263320A1; Han Y. Ban, et al., “Kernel Flow: A High Channel Count Scalable TD-fNIRS System,” SPIE Photonics West Conference (Mar. 6, 2021); and Han Y. Ban, et al., “Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system,” Journal of Biomedical Optics (Jan. 18, 2022), which applications and publications are incorporated herein by reference in their entirety.

To illustrate, FIGS. 2-4, 5A, and 5B, show various optical measurement systems and related components that may implement brain interface system 102. The optical measurement systems described herein are merely illustrative of the many different optical-based brain interface systems that may be used in accordance with the systems and methods described herein.

FIG. 2 shows an optical measurement system 200 that may be configured to perform an optical measurement operation with respect to a body 202 (e.g., the brain). Optical measurement system 200 may, in some examples, be portable and/or wearable by a user.

In some examples, optical measurement operations performed by optical measurement system 200 are associated with a time domain-based optical measurement technique. Example time domain-based optical measurement techniques include, but are not limited to, time-correlated single-photon counting (TCSPC), time domain near infrared spectroscopy (TD-NIRS), time domain diffusive correlation spectroscopy (TD-DCS), and time domain digital optical tomography (TD-DOT).

Optical measurement system 200 (e.g., an optical measurement system that is implemented by a wearable device or other configuration, and that employs a time domain-based (e.g., TD-NIRS) measurement technique) may detect blood oxygenation levels and/or blood volume levels by measuring the change in shape of laser pulses after they have passed through target tissue, e.g., brain, muscle, finger, etc. As used herein, a shape of laser pulses refers to a temporal shape, as represented for example by a histogram generated by a time-to-digital converter (TDC) coupled to an output of a photodetector, as will be described more fully below.

As shown, optical measurement system 200 includes a detector 204 that includes a plurality of individual photodetectors (e.g., photodetector 206), a processor 208 coupled to detector 204, a light source 210, a controller 212, and optical conduits 214 and 216 (e.g., light pipes). However, one or more of these components may not, in certain embodiments, be considered to be a part of optical measurement system 200. For example, in implementations where optical measurement system 200 is wearable by a user, processor 208 and/or controller 212 may in some embodiments be separate from optical measurement system 200 and not configured to be worn by the user.

Detector 204 may include any number of photodetectors 206 as may serve a particular implementation, such as 2^(n) photodetectors (e.g., 256, 512, . . . , 26384, etc.), where n is an integer greater than or equal to one (e.g., 4, 5, 8, 20, 21, 24, etc.). Photodetectors 206 may be arranged in any suitable manner.

Photodetectors 206 may each be implemented by any suitable circuit configured to detect individual photons of light incident upon photodetectors 206. For example, each photodetector 206 may be implemented by a single photon avalanche diode (SPAD) circuit and/or other circuitry as may serve a particular implementation. The SPAD circuit may be gated in any suitable manner or be configured to operate in a free running mode with passive quenching. For example, photodetectors 206 may be configured to operate in a free-running mode such that photodetectors 206 are not actively armed and disarmed (e.g., at the end of each predetermined gated time window). In contrast, while operating in the free-running mode, photodetectors 206 may be configured to reset within a configurable time period after an occurrence of a photon detection event (i.e., after photodetector 206 detects a photon) and immediately begin detecting new photons. However, only photons detected within a desired time window (e.g., during each gated time window) may be included in the histogram that represents a light pulse response of the target (e.g., a temporal point spread function (TPSF)). The terms histogram and TPSF are used interchangeably herein to refer to a light pulse response of a target.

Processor 208 may be implemented by one or more physical processing (e.g., computing) devices. In some examples, processor 208 may execute instructions (e.g., software) configured to perform one or more of the operations described herein.

Light source 210 may be implemented by any suitable component configured to generate and emit light. For example, light source 210 may be implemented by one or more laser diodes, distributed feedback (DFB) lasers, super luminescent diodes (SLDs), light emitting diodes (LEDs), diode-pumped solid-state (DPSS) lasers, super luminescent light emitting diodes (sLEDs), vertical-cavity surface-emitting lasers (VCSELs), titanium sapphire lasers, micro light emitting diodes (mLEDs), and/or any other suitable laser or light source. In some examples, the light emitted by light source 210 is high coherence light (e.g., light that has a coherence length of at least 5 centimeters) at a predetermined center wavelength.

Light source 210 is controlled by controller 212, which may be implemented by any suitable computing device (e.g., processor 208), integrated circuit, and/or combination of hardware and/or software as may serve a particular implementation. In some examples, controller 212 is configured to control light source 210 by turning light source 210 on and off and/or setting an intensity of light generated by light source 210. Controller 212 may be manually operated by a user, or may be programmed to control light source 210 automatically.

Light emitted by light source 210 may travel via an optical conduit 214 (e.g., a light pipe, a single-mode optical fiber, and/or or a multi-mode optical fiber) to body 202 of a subject. Body 202 may include any suitable turbid medium. For example, in some implementations, body 202 is a brain or any other body part of a human or other animal. Alternatively, body 202 may be a non-living object. For illustrative purposes, it will be assumed in the examples provided herein that body 202 is a human brain.

As indicated by arrow 220, the light emitted by light source 210 enters body 202 at a first location 222 on body 202. Accordingly, a distal end of optical conduit 214 may be positioned at (e.g., right above, in physical contact with, or physically attached to) first location 222 (e.g., to a scalp of the subject). In some examples, the light may emerge from optical conduit 214 and spread out to a certain spot size on body 202 to fall under a predetermined safety limit. At least a portion of the light indicated by arrow 220 may be scattered within body 202.

As used herein, “distal” means nearer, along the optical path of the light emitted by light source 210 or the light received by detector 204, to the target (e.g., within body 202) than to light source 210 or detector 204. Thus, the distal end of optical conduit 214 is nearer to body 202 than to light source 210, and the distal end of optical conduit 216 is nearer to body 202 than to detector 204. Additionally, as used herein, “proximal” means nearer, along the optical path of the light emitted by light source 210 or the light received by detector 204, to light source 210 or detector 204 than to body 202. Thus, the proximal end of optical conduit 214 is nearer to light source 210 than to body 202, and the proximal end of optical conduit 216 is nearer to detector 204 than to body 202.

As shown, the distal end of optical conduit 216 (e.g., a light pipe, a light guide, a waveguide, a single-mode optical fiber, and/or a multi-mode optical fiber) is positioned at (e.g., right above, in physical contact with, or physically attached to) output location 226 on body 202. In this manner, optical conduit 216 may collect at least a portion of the scattered light (indicated as light 224) as it exits body 202 at location 226 and carry light 224 to detector 204. Light 224 may pass through one or more lenses and/or other optical elements (not shown) that direct light 224 onto each of the photodetectors 206 included in detector 204. In cases where optical conduit 216 is implemented by a light guide, the light guide may be spring loaded and/or have a cantilever mechanism to allow for conformably pressing the light guide firmly against body 202.

Photodetectors 206 may be connected in parallel in detector 204. An output of each of photodetectors 206 may be accumulated to generate an accumulated output of detector 204. Processor 208 may receive the accumulated output and determine, based on the accumulated output, a temporal distribution of photons detected by photodetectors 206. Processor 208 may then generate, based on the temporal distribution, a histogram representing a light pulse response of a target (e.g., brain tissue, blood flow, etc.) in body 202. Such a histogram is illustrative of the various types of brain activity measurements that may be performed by brain interface system 102.

FIG. 3 shows an exemplary optical measurement system 300 in accordance with the principles described herein. Optical measurement system 300 may be an implementation of optical measurement system 200 and, as shown, includes a wearable assembly 302, which includes N light sources 304 (e.g., light sources 304-1 through 304-N) and M detectors 306 (e.g., detectors 306-1 through 306-M). Optical measurement system 300 may include any of the other components of optical measurement system 200 as may serve a particular implementation. N and M may each be any suitable value (i.e., there may be any number of light sources 304 and detectors 306 included in optical measurement system 300 as may serve a particular implementation).

Light sources 304 are each configured to emit light (e.g., a sequence of light pulses) and may be implemented by any of the light sources described herein. Detectors 306 may each be configured to detect arrival times for photons of the light emitted by one or more light sources 304 after the light is scattered by the target. For example, a detector 306 may include a photodetector configured to generate a photodetector output pulse in response to detecting a photon of the light and a time-to-digital converter (TDC) configured to record a timestamp symbol in response to an occurrence of the photodetector output pulse, the timestamp symbol representative of an arrival time for the photon (i.e., when the photon is detected by the photodetector).

Wearable assembly 302 may be implemented by any of the wearable devices, modular assemblies, and/or wearable units described herein. For example, wearable assembly 302 may be implemented by a wearable device (e.g., headgear) configured to be worn on a user's head. Wearable assembly 302 may additionally or alternatively be configured to be worn on any other part of a user's body.

Optical measurement system 300 may be modular in that one or more components of optical measurement system 300 may be removed, changed out, or otherwise modified as may serve a particular implementation. As such, optical measurement system 300 may be configured to conform to three-dimensional surface geometries, such as a user's head. Exemplary modular optical measurement systems comprising a plurality of wearable modules are described in more detail in one or more of the patent applications incorporated herein by reference.

FIG. 4 shows an illustrative modular assembly 400 that may implement optical measurement system 300. Modular assembly 400 is illustrative of the many different implementations of optical measurement system 300 that may be realized in accordance with the principles described herein.

As shown, modular assembly 400 includes a plurality of modules 402 (e.g., modules 402-1 through 402-3) physically distinct one from another. While three modules 402 are shown to be included in modular assembly 400, in alternative configurations, any number of modules 402 (e.g., a single module up to sixteen or more modules) may be included in modular assembly 400.

Each module 402 includes a light source (e.g., light source 404-1 of module 402-1 and light source 404-2 of module 402-2) and a plurality of detectors (e.g., detectors 406-1 through 406-6 of module 402-1). In the particular implementation shown in FIG. 4 , each module 402 includes a single light source and six detectors. Each light source is labeled “S” and each detector is labeled “D”.

Each light source depicted in FIG. 4 may be implemented by one or more light sources similar to light source 210 and may be configured to emit light directed at a target (e.g., the brain).

Each light source depicted in FIG. 4 may be located at a center region of a surface of the light source's corresponding module. For example, light source 404-1 is located at a center region of a surface 408 of module 402-1. In alternative implementations, a light source of a module may be located away from a center region of the module.

Each detector depicted in FIG. 4 may implement or be similar to detector 204 and may include a plurality of photodetectors (e.g., SPADs) as well as other circuitry (e.g., TDCs), and may be configured to detect arrival times for photons of the light emitted by one or more light sources after the light is scattered by the target.

The detectors of a module may be distributed around the light source of the module. For example, detectors 406 of module 402-1 are distributed around light source 404-1 on surface 408 of module 402-1. In this configuration, detectors 406 may be configured to detect photon arrival times for photons included in light pulses emitted by light source 404-1. In some examples, one or more detectors 406 may be close enough to other light sources to detect photon arrival times for photons included in light pulses emitted by the other light sources. For example, because detector 406-3 is adjacent to module 402-2, detector 406-3 may be configured to detect photon arrival times for photons included in light pulses emitted by light source 404-2 (in addition to detecting photon arrival times for photons included in light pulses emitted by light source 404-1).

In some examples, the detectors of a module may all be equidistant from the light source of the same module. In other words, the spacing between a light source (i.e., a distal end portion of a light source optical conduit) and the detectors (i.e., distal end portions of optical conduits for each detector) are maintained at the same fixed distance on each module to ensure homogeneous coverage over specific areas and to facilitate processing of the detected signals. The fixed spacing also provides consistent spatial (lateral and depth) resolution across the target area of interest, e.g., brain tissue. Moreover, maintaining a known distance between the light source, e.g., light emitter, and the detector allows subsequent processing of the detected signals to infer spatial (e.g., depth localization, inverse modeling) information about the detected signals. Detectors of a module may be alternatively disposed on the module as may serve a particular implementation.

In some examples, modular assembly 400 can conform to a three-dimensional (3D) surface of the human subject's head, maintain tight contact of the detectors with the human subject's head to prevent detection of ambient light, and maintain uniform and fixed spacing between light sources and detectors. The wearable module assemblies may also accommodate a large variety of head sizes, from a young child's head size to an adult head size, and may accommodate a variety of head shapes and underlying cortical morphologies through the conformability and scalability of the wearable module assemblies. These exemplary modular assemblies and systems are described in more detail in U.S. patent application Ser. No. 17/176,460, filed Feb. 16, 2021 and issued as U.S. Pat. No. 11,096,620, U.S. patent application Ser. No. 17/176,470, filed Feb. 16, 2021 and published as US2021/0259619A1, U.S. patent application Ser. No. 17/176,487, filed Feb. 16, 2021 and published as US2021/0259632A1, U.S. patent application Ser. No. 17/176,539, filed Feb. 16, 2021 and published as US2021/0259620A1, U.S. patent application Ser. No. 17/176,560, filed Feb. 16, 2021 and published as US2021/0259597A1, and U.S. patent application Ser. No. 17/176,466, filed Feb. 16, 2021 and published as US2021/0263320A1, which applications are incorporated herein by reference in their respective entireties.

In FIG. 4 , modules 402 are shown to be adjacent to and touching one another. Modules 402 may alternatively be spaced apart from one another. For example, FIGS. 5A-5B show an exemplary implementation of modular assembly 400 in which modules 402 are configured to be inserted into individual slots 502 (e.g., slots 502-1 through 502-3, also referred to as cutouts) of a wearable assembly 504. In particular, FIG. 5A shows the individual slots 502 of the wearable assembly 504 before modules 402 have been inserted into respective slots 502, and FIG. 5B shows wearable assembly 504 with individual modules 402 inserted into respective individual slots 502.

Wearable assembly 504 may implement wearable assembly 302 and may be configured as headgear and/or any other type of device configured to be worn by a user.

As shown in FIG. 5A, each slot 502 is surrounded by a wall (e.g., wall 506) such that when modules 402 are inserted into their respective individual slots 502, the walls physically separate modules 402 one from another. In alternative embodiments, a module (e.g., module 402-1) may be in at least partial physical contact with a neighboring module (e.g., module 402-2).

Each of the modules described herein may be inserted into appropriately shaped slots or cutouts of a wearable assembly, as described in connection with FIGS. 5A-5B. However, for ease of explanation, such wearable assemblies are not shown in the figures.

As shown in FIGS. 4 and 5B, modules 402 may have a hexagonal shape. Modules 402 may alternatively have any other suitable geometry (e.g., in the shape of a pentagon, octagon, square, rectangular, circular, triangular, free-form, etc.).

As another example, brain interface system 102 may be implemented by a wearable multimodal measurement system configured to perform both optical-based brain data acquisition operations and electrical-based brain data acquisition operations, such as any of the wearable multimodal measurement systems described in U.S. Patent Application Publication Nos. 2021/0259638 and 2021/0259614, which publications are incorporated herein by reference in their respective entireties.

To illustrate, FIGS. 6-7 show various multimodal measurement systems that may implement brain interface system 102. The multimodal measurement systems described herein are merely illustrative of the many different multimodal-based brain interface systems that may be used in accordance with the systems and methods described herein.

FIG. 6 shows an exemplary multimodal measurement system 600 in accordance with the principles described herein. Multimodal measurement system 600 may at least partially implement optical measurement system 200 and, as shown, includes a wearable assembly 602 (which is similar to wearable assembly 302), which includes N light sources 604 (e.g., light sources 604-1 through 604-N, which are similar to light sources 304), M detectors 606 (e.g., detectors 606-1 through 606-M, which are similar to detectors 306), and X electrodes (e.g., electrodes 608-1 through 608-X). Multimodal measurement system 600 may include any of the other components of optical measurement system 200 as may serve a particular implementation. N, M, and X may each be any suitable value (i.e., there may be any number of light sources 604, any number of detectors 606, and any number of electrodes 608 included in multimodal measurement system 600 as may serve a particular implementation).

Electrodes 608 may be configured to detect electrical activity within a target (e.g., the brain). Such electrical activity may include electroencephalogram (EEG) activity and/or any other suitable type of electrical activity as may serve a particular implementation. In some examples, electrodes 608 are all conductively coupled to one another to create a single channel that may be used to detect electrical activity. Alternatively, at least one electrode included in electrodes 608 is conductively isolated from a remaining number of electrodes included in electrodes 608 to create at least two channels that may be used to detect electrical activity.

FIG. 7 shows an illustrative modular assembly 700 that may implement multimodal measurement system 600. As shown, modular assembly 700 includes a plurality of modules 702 (e.g., modules 702-1 through 702-3). While three modules 702 are shown to be included in modular assembly 700, in alternative configurations, any number of modules 702 (e.g., a single module up to sixteen or more modules) may be included in modular assembly 700. Moreover, while each module 702 has a hexagonal shape, modules 702 may alternatively have any other suitable geometry (e.g., in the shape of a pentagon, octagon, square, rectangular, circular, triangular, free-form, etc.).

Each module 702 includes a light source (e.g., light source 704-1 of module 702-1 and light source 704-2 of module 702-2) and a plurality of detectors (e.g., detectors 706-1 through 706-6 of module 702-1). In the particular implementation shown in FIG. 7 , each module 702 includes a single light source and six detectors. Alternatively, each module 702 may have any other number of light sources (e.g., two light sources) and any other number of detectors. The various components of modular assembly 700 shown in FIG. 7 are similar to those described in connection with FIG. 4 .

As shown, modular assembly 700 further includes a plurality of electrodes 710 (e.g., electrodes 710-1 through 710-3), which may implement electrodes 608. Electrodes 710 may be located at any suitable location that allows electrodes 710 to be in physical contact with a surface (e.g., the scalp and/or skin) of a body of a user. For example, in modular assembly 700, each electrode 710 is on a module surface configured to face a surface of a user's body when modular assembly 700 is worn by the user. To illustrate, electrode 710-1 is on surface 708 of module 702-1. Moreover, in modular assembly 700, electrodes 710 are located in a center region of each module 702 and surround each module's light source 704. Alternative locations and configurations for electrodes 710 are possible.

As another example, brain interface system 102 may be implemented by a wearable magnetic field measurement system configured to perform magnetic field-based brain data acquisition operations, such as any of the magnetic field measurement systems described in U.S. patent application Ser. No. 16/862,879, filed Apr. 30, 2020 and published as US20200348368A1; U.S. Provisional Application No. 63/170,892, filed Apr. 5, 2021, U.S. patent application Ser. No. 17/338,429, filed Jun. 3, 2021, and Ethan J. Pratt, et al., “Kernel Flux: A Whole-Head 432-Magnetometer Optically-Pumped Magnetoencephalography (OP-MEG) System for Brain Activity Imaging During Natural Human Experiences,” SPIE Photonics West Conference (Mar. 6, 2021), which applications and publications are incorporated herein by reference in their entirety. In some examples, any of the magnetic field measurement systems described herein may be used in a magnetically shielded environment which allows for natural user movement as described for example in U.S. Provisional Patent Application No. 63/076,015, filed Sep. 9, 2020, and U.S. patent application Ser. No. 17/328,235, filed May 24, 2021 and published as US2021/0369166A1, which applications are incorporated herein by reference in their entirety.

FIG. 8 shows an exemplary magnetic field measurement system 800 (“system 800”) that may implement brain interface system 102. As shown, system 800 includes a wearable sensor unit 802 and a controller 804. Wearable sensor unit 802 includes a plurality of magnetometers 806-1 through 806-N (collectively “magnetometers 806”, also referred to as optically pumped magnetometer (OPM) modular assemblies as described below) and a magnetic field generator 808. Wearable sensor unit 802 may include additional components (e.g., one or more magnetic field sensors, position sensors, orientation sensors, accelerometers, image recorders, detectors, etc.) as may serve a particular implementation. System 800 may be used in magnetoencephalography (MEG) and/or any other application that measures relatively weak magnetic fields.

Wearable sensor unit 802 is configured to be worn by a user (e.g., on a head of the user). In some examples, wearable sensor unit 802 is portable. In other words, wearable sensor unit 802 may be small and light enough to be easily carried by a user and/or worn by the user while the user moves around and/or otherwise performs daily activities, or may be worn in a magnetically shielded environment which allows for natural user movement as described more fully in U.S. Provisional Patent Application No. 63/076,015, and U.S. patent application Ser. No. 17/328,235, filed May 24, 2021 and published as US2021/0369166A1, previously incorporated by reference.

Any suitable number of magnetometers 806 may be included in wearable sensor unit 802. For example, wearable sensor unit 802 may include an array of nine, sixteen, twenty-five, or any other suitable plurality of magnetometers 806 as may serve a particular implementation.

Magnetometers 806 may each be implemented by any suitable combination of components configured to be sensitive enough to detect a relatively weak magnetic field (e.g., magnetic fields that come from the brain). For example, each magnetometer may include a light source, a vapor cell such as an alkali metal vapor cell (the terms “cell”, “gas cell”, “vapor cell”, and “vapor gas cell” are used interchangeably herein), a heater for the vapor cell, and a photodetector (e.g., a signal photodiode). Examples of suitable light sources include, but are not limited to, a diode laser (such as a vertical-cavity surface-emitting laser (VCSEL), distributed Bragg reflector laser (DBR), or distributed feedback laser (DFB)), light-emitting diode (LED), lamp, or any other suitable light source. In some embodiments, the light source may include two light sources: a pump light source and a probe light source.

Magnetic field generator 808 may be implemented by one or more components configured to generate one or more compensation magnetic fields that actively shield magnetometers 806 (including respective vapor cells) from ambient background magnetic fields (e.g., the Earth's magnetic field, magnetic fields generated by nearby magnetic objects such as passing vehicles, electrical devices and/or other field generators within an environment of magnetometers 806, and/or magnetic fields generated by other external sources). For example, magnetic field generator 808 may include one or more coils configured to generate compensation magnetic fields in the Z direction, X direction, and/or Y direction (all directions are with respect to one or more planes within which the magnetic field generator 808 is located). The compensation magnetic fields are configured to cancel out, or substantially reduce, ambient background magnetic fields in a magnetic field sensing region with minimal spatial variability.

Controller 804 is configured to interface with (e.g., control an operation of, receive signals from, etc.) magnetometers 806 and the magnetic field generator 808. Controller 804 may also interface with other components that may be included in wearable sensor unit 802.

In some examples, controller 804 is referred to herein as a “single” controller 804. This means that only one controller is used to interface with all of the components of wearable sensor unit 802. For example, controller 804 may be the only controller that interfaces with magnetometers 806 and magnetic field generator 808. It will be recognized, however, that any number of controllers may interface with components of magnetic field measurement system 800 as may suit a particular implementation.

As shown, controller 804 may be communicatively coupled to each of magnetometers 806 and magnetic field generator 808. For example, FIG. 8 shows that controller 804 is communicatively coupled to magnetometer 806-1 by way of communication link 810-1, to magnetometer 806-2 by way of communication link 810-2, to magnetometer 806-N by way of communication link 810-N, and to magnetic field generator 808 by way of communication link 812. In this configuration, controller 804 may interface with magnetometers 806 by way of communication links 810-1 through 810-N (collectively “communication links 810”) and with magnetic field generator 808 by way of communication link 812.

Communication links 810 and communication link 812 may be implemented by any suitable wired connection as may serve a particular implementation. For example, communication links 810 may be implemented by one or more twisted pair cables while communication link 812 may be implemented by one or more coaxial cables. Alternatively, communication links 810 and communication link 812 may both be implemented by one or more twisted pair cables. In some examples, the twisted pair cables may be unshielded.

Controller 804 may be implemented in any suitable manner. For example, controller 804 may be implemented by a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP), a microcontroller, and/or other suitable circuit together with various control circuitry.

In some examples, controller 804 is implemented on one or more printed circuit boards (PCBs) included in a single housing. In cases where controller 804 is implemented on a PCB, the PCB may include various connection interfaces configured to facilitate communication links 810 and 812. For example, the PCB may include one or more twisted pair cable connection interfaces to which one or more twisted pair cables may be connected (e.g., plugged into) and/or one or more coaxial cable connection interfaces to which one or more coaxial cables may be connected (e.g., plugged into).

In some examples, controller 804 may be implemented by or within a computing device.

In some examples, a wearable magnetic field measurement system may include a plurality of optically pumped magnetometer (OPM) modular assemblies, which OPM modular assemblies are enclosed within a housing sized to fit into a headgear (e.g., brain interface system 102) for placement on a head of a user (e.g., human subject). The OPM modular assembly is designed to enclose the elements of the OPM optics, vapor cell, and detectors in a compact arrangement that can be positioned close to the head of the human subject. The headgear may include an adjustment mechanism used for adjusting the headgear to conform with the human subject's head. These exemplary OPM modular assemblies and systems are described in more detail in U.S. Provisional Patent Application No. 63/170,892, filed Apr. 5, 2021, and U.S. patent application Ser. No. 17/338,429, filed Jun. 3, 2021, previously incorporated by reference.

At least some of the elements of the OPM modular assemblies, systems which can employ the OPM modular assemblies, and methods of making and using the OPM modular assemblies have been disclosed in U.S. Patent Application Publications Nos. 2020/0072916; 2020/0056263; 2020/0025844; 2020/0057116; 2019/0391213; 2020/0088811; 2020/0057115; 2020/0109481; 2020/0123416; 2020/0191883; 2020/0241094; 2020/0256929; 2020/0309873; 2020/0334559; 2020/0341081; 2020/0381128; 2020/0400763; 2021/0011094; 2021/0015385; 2021/0041512; 2021/0041513; 2021/0063510; and 20210139742, and U.S. Provisional Patent Application Ser. Nos. 62/689,696; 62/699,596; 62/719,471; 62/719,475; 62/719,928; 62/723,933; 62/732,327; 62/732,791; 62/741,777; 62/743,343; 62/747,924; 62/745,144; 62/752,067; 62/776,895; 62/781,418; 62/796,958; 62/798,209; 62/798,330; 62/804,539; 62/826,045; 62/827,390; 62/836,421; 62/837,574; 62/837,587; 62/842,818; 62/855,820; 62/858,636; 62/860,001; 62/865,049; 62/873,694; 62/874,887; 62/883,399; 62/883,406; 62/888,858; 62/895,197; 62/896,929; 62/898,461; 62/910,248; 62/913,000; 62/926,032; 62/926,043; 62/933,085; 62/960,548; 62/971,132; 63/031,469; 63/052,327; 63/076,015; 63/076,880; 63/080,248; 63/135,364; 63/136,415; and 63/170,892, all of which are incorporated herein by reference in their entireties.

In some examples, one or more components of brain interface system 102, FIG. 1 , (e.g., one or more computing devices) may be configured to be located off the head of the user.

In each of the different brain interface system implementations described herein, the brain activity data may be based on the type of operations performed by the different brain interface system implementations. For example, if brain interface system 102 is implemented by an optical measurement system configured to perform optical-based brain data acquisition operations, the brain activity data may be based on the optical-based brain data acquisition operations. As another example, if brain interface system 102 is implemented by a multimodal measurement system configured to perform optical-based brain data acquisition operations and electrical-based brain data acquisition operations, the brain activity data may be based on the optical-based brain data acquisition operations and the electrical-based brain data acquisition operations. As another example, if brain interface system 102 is implemented by a magnetic field measurement system configured to perform magnetic field-based brain data acquisition operations, the brain activity data may be based on the magnetic field-based brain data acquisition operations.

In some examples, computing device 106 may be configured to obtain game data representative of one or more characteristics of the electronic game, and use the game data in combination with the brain activity data to modify the attribute of the electronic game. The one or more characteristics represented by the game data may include a current difficulty level of the electronic game, a current scene of the electronic game that the user is experiencing, a score achieved by the user while playing the electronic game, an attribute of a user profile used by the user to play the electronic game, and/or any other characteristic of the electronic game.

To illustrate, FIG. 9 shows an exemplary configuration 900 in which a gaming system 902 presents an electronic game to a user and outputs game data associated with the electronic game. While FIG. 9 shows the game data being output by gaming system 902, the game data may alternatively be output by any other computing device as may serve a particular implementation.

As shown, computing device 106 may obtain the game data (e.g., by accessing the game data in substantially real time as the game data is output by gaming system 902). In the example of FIG. 9 , the game control data output by computing device 106 is based on both the brain activity data and the game data.

As shown, the game control data may be fed back into gaming system 902. In this manner, the game control data may modify one or more attributes of electronic game being presented by gaming system 902.

In some examples, computing device 106 may be configured to obtain sensor data associated with the user from a sensor not included in the brain interface system and further base the modification of the attribute of the electronic game on the sensor data.

To illustrate, FIG. 10 shows an exemplary configuration 1000 in which a sensor 1002 outputs sensor data associated with the user. As shown, computing device 106 may obtain the sensor data (e.g., by receiving or otherwise accessing the sensor data in substantially real time as the sensor data is output by sensor 1002). In the example of FIG. 10 , the game control data output by computing device 106 is based on both the brain activity data and the sensor data.

Sensor 1002 may be implemented in any suitable manner. For example, sensor 1002 may be implemented by one or more sensors that perform eye tracking, electrodermal activity (EDA)/conductance, pupillometry, heart rate, heart rate variability, and/or pulse oximetry. Additionally or alternatively, sensor 1002 may be implemented by one or more microphones configured to detect ambient sound of the user while the user plays the electronic game, one or more inertial motion units (IMUs) configured to detect movement by the user while the user plays the electronic game, etc. Output from one or more of these sensors may be fused to provide a wholistic view of the user's experience or performance while playing the electronic game.

In some examples, computing device 106 may be configured to determine, based on the brain activity data and/or any of the other types of data described herein, a current mental state of the user and obtain data representative of a desired mental state of the user. Such mental states are described herein. Computing device 106 may accordingly modify the attribute of the electronic game by setting the attribute to a value configured to change the current mental state of the user to the desired mental state of the user.

For example, if the user indicates (e.g., by providing input) that the user desires to have a non-stressed mental state, a difficulty level of the electronic game may be made easier if the user is not doing well and therefore feeling frustrated. As another example, a difficulty level and/or any other aspect of the electronic game may be adjusted based on user engagement or emotional response as indicated by the brain activity data.

FIG. 11 shows an illustrative configuration 1100 in which computing device 106 is configured to implement a machine learning model 1102 to perform one or more operations based on the brain activity data output by brain interface system 102. Such operations may include any of the operations described herein (e.g., generating game control data, presenting content, etc.). As another example, computing device 106 may be configured to use machine learning model 1102 to generate predicted user action data based on the brain activity data output by brain interface system 102. The predicted user action data may be representative of one or more predicted actions that the user may take while playing an electronic game. For example, if the brain activity data indicates that the user is becoming mentally fatigued, the predicted user action data may indicate that the user will likely take one or more relatively risky actions during the electronic game that result in sub-optimal performance by the user during the electronic game. The predicted user action data may be presented to the user and/or one or more other people to assist the user in optimizing game play and/or for one or more other reasons as may serve a particular implementation.

Machine learning model 1102 may be configured to perform any suitable machine learning heuristic (also referred to as artificial intelligence heuristic) to input data, which may be in either the time or frequency domains. Machine learning model 1102 may accordingly be supervised and/or unsupervised as may serve a particular implementation and may be configured to implement one or more decision tree learning algorithms, association rule learning algorithms, artificial neural network learning algorithms, deep learning algorithms, bitmap algorithms, and/or any other suitable data analysis technique as may serve a particular implementation.

In some examples, machine learning model 1102 is implemented by one or more neural networks, such as one or more deep convolutional neural networks (CNN) using internal memories of its respective kernels (filters), recurrent neural networks (RNN), and/or long/short term memory neural networks (LSTM). Machine learning model 1102 may be multi-layer. For example, machine learning model 1102 may be implemented by a neural network that includes an input layer, one or more hidden layers, and an output layer. Machine learning model 1102 may be trained in any suitable manner.

In some examples, computing device 106 may be configured to present, by way of a graphical user interface, content representative of the brain activity data to the user and/or to other people watching the user play the electronic game (e.g., via an online platform such as Twitch).

For example, FIG. 12 shows an exemplary graphical user interface 1200 configured to display game content 1202 (e.g., one or more graphics associated with the electronic game). As shown, brain activity content 1204 representative of the brain activity data may also be displayed together with the game content 1202. Graphical user interface 1200 may be presented to the user (e.g., by way of a display included in or connected to computing device 106) and/or to one or more other users (e.g., by way of a network, such as the Internet).

Brain activity content 1204 may be presented in any suitable form. For example, FIG. 13 shows an implementation 1300 of brain activity content 1204 in which the brain activity is displayed as a two-dimensional (2D) map. Alternatively, the brain activity may be displayed as an interactive three-dimensional (3D) visualization. Using either the 2D or 3D representation, an observer could explore the different regions of activation while the user plays and interacts with the electronic game.

FIG. 14 shows another implementation 1400 of brain activity content 1204 in which the brain activity could be quantified by regions of interest with a graphical representation of the magnitude of signal in each region shown, e.g., by a bar. The brain activity may additionally or alternatively be displayed using an abstract representation similar to an audio visualizer.

Additionally or alternatively, brain activity content 1204 may be presented in the form of a score, a game report, etc. For example, the user may receive a game report that shows the user's biological system responses during gameplay. The report could include brain data only or extend to brain and physiological data if multiple sensors are combined. This report could suggest how a user could improve a score or game play experience, or highlight times when brain activity was optimized for performance. The game report might also suggest which parts of the game a player found enjoyable or stressful. A game report could provide a comparison to other users with similar brain activity patterns or physiological response patterns, or suggest other games that a player might find enjoyable based on their brain activity or physiological responses to a game. Game reports could also be compared for the same person over time to show improvement or changes in cognitive strategies during gaming.

In some examples, the physiological response data can be obtained if multiple sensors are combined using other types of optical measurement systems having form factors that can be worn (e.g., on the finger, wrist, feet, toes, forehead, chest, ear, or any other body part). Such optical measurement systems are described more fully in U.S. Provisional Patent Application No. 63/134,479, filed Jan. 6, 2021; U.S. Provisional Patent Application No. 63/154,116, filed Feb. 26, 2021; U.S. Provisional Patent Application No. 63/160,995, filed Mar. 15, 2021; U.S. Provisional Patent Application No. 63/179,080, filed Apr. 23, 2021; U.S. Provisional Patent Application No. 63/154,127, filed Feb. 26, 2021; U.S. Provisional Patent Application No. 63/191,822, filed May 21, 2021; U.S. Provisional Patent Application No. 63/154,131, filed Feb. 26, 2021; and U.S. Provisional Patent Application No. 63/196,917, filed Jun. 4, 2021, which applications are incorporated herein by reference in their entirety.

In some examples, the physiological response data can be obtained using a wearable extended reality-based neuroscience analysis system as described in U.S. Provisional Patent Application No. 63/139,469, filed Jan. 20, 2021, and U.S. Provisional Patent Application No. 63/139,478, filed Jan. 20, 2021, which applications are incorporated herein by reference in their entirety.

FIG. 15 illustrates an exemplary method 1500. While FIG. 15 illustrates exemplary operations according to one embodiment, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in FIG. 15 . One or more of the operations shown in FIG. 15 may be performed by computing device 106 and/or any implementation thereof. Each of the operations illustrated in FIG. 15 may be performed in any suitable manner.

At operation 1502, a computing device may obtain, from a brain interface system configured to be worn by a user while the user plays an electronic game, brain activity data representative of brain activity of the user while the user concurrently plays the electronic game.

At operation 1504, the computing device may modify, based on the brain activity data, an attribute of the electronic game.

In some examples, a non-transitory computer-readable medium storing computer-readable instructions may be provided in accordance with the principles described herein. The instructions, when executed by a processor of a computing device, may direct the processor and/or computing device to perform one or more operations, including one or more of the operations described herein. Such instructions may be stored and/or transmitted using any of a variety of known computer-readable media.

A non-transitory computer-readable medium as referred to herein may include any non-transitory storage medium that participates in providing data (e.g., instructions) that may be read and/or executed by a computing device (e.g., by a processor of a computing device). For example, a non-transitory computer-readable medium may include, but is not limited to, any combination of non-volatile storage media and/or volatile storage media. Exemplary non-volatile storage media include, but are not limited to, read-only memory, flash memory, a solid-state drive, a magnetic storage device (e.g. a hard disk, a floppy disk, magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and an optical disc (e.g., a compact disc, a digital video disc, a Blu-ray disc, etc.). Exemplary volatile storage media include, but are not limited to, RAM (e.g., dynamic RAM).

FIG. 16 illustrates an exemplary computing device 1600 that may be specifically configured to perform one or more of the processes described herein. Any of the systems, units, computing devices, and/or other components described herein may be implemented by computing device 1600.

As shown in FIG. 16 , computing device 1600 may include a communication interface 1602, a processor 1604, a storage device 1606, and an input/output (“I/O”) module 1608 communicatively connected one to another via a communication infrastructure 1610. While an exemplary computing device 1600 is shown in FIG. 16 , the components illustrated in FIG. 16 are not intended to be limiting. Additional or alternative components may be used in other embodiments. Components of computing device 1600 shown in FIG. 16 will now be described in additional detail.

Communication interface 1602 may be configured to communicate with one or more computing devices. Examples of communication interface 1602 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio/video connection, and any other suitable interface.

Processor 1604 generally represents any type or form of processing unit capable of processing data and/or interpreting, executing, and/or directing execution of one or more of the instructions, processes, and/or operations described herein. Processor 1604 may perform operations by executing computer-executable instructions 1612 (e.g., an application, software, code, and/or other executable data instance) stored in storage device 1606.

Storage device 1606 may include one or more data storage media, devices, or configurations and may employ any type, form, and combination of data storage media and/or device. For example, storage device 1606 may include, but is not limited to, any combination of the non-volatile media and/or volatile media described herein. Electronic data, including data described herein, may be temporarily and/or permanently stored in storage device 1606. For example, data representative of computer-executable instructions 1612 configured to direct processor 1604 to perform any of the operations described herein may be stored within storage device 1606. In some examples, data may be arranged in one or more databases residing within storage device 1606.

I/O module 1608 may include one or more I/O modules configured to receive user input and provide user output. I/O module 1608 may include any hardware, firmware, software, or combination thereof supportive of input and output capabilities. For example, I/O module 1608 may include hardware and/or software for capturing user input, including, but not limited to, a keyboard or keypad, a touchscreen component (e.g., touchscreen display), a receiver (e.g., an RF or infrared receiver), motion sensors, and/or one or more input buttons.

I/O module 1608 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, I/O module 1608 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation.

An illustrative system includes a brain interface system configured to be worn by a user and to output brain activity data representative of brain activity of the user while the user plays an electronic game; and a computing device configured to obtain the brain activity data, and modify, based on the brain activity data, an attribute of the electronic game.

An illustrative apparatus includes a memory storing instructions and a processor communicatively coupled to the memory and configured to execute the instructions to: obtain, from a brain interface system configured to be worn by a user while the user concurrently plays an electronic game, brain activity data representative of brain activity of the user while the user plays the electronic game, and modify, based on the brain activity data, an attribute of the electronic game.

An illustrative method includes obtaining, by a computing device from a brain interface system configured to be worn by a user while the user concurrently plays an electronic game, brain activity data representative of brain activity of the user while the user plays the electronic game; and modifying, by the computing device based on the brain activity data, an attribute of the electronic game.

An illustrative non-transitory computer-readable medium storing instructions that, when executed, direct a processor of a computing device to: obtain, from a brain interface system configured to be worn by a user while the user concurrently plays an electronic game, brain activity data representative of brain activity of the user while the user plays the electronic game; and modify, based on the brain activity data, an attribute of the electronic game.

In the preceding description, various exemplary embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the scope of the invention as set forth in the claims that follow. For example, certain features of one embodiment described herein may be combined with or substituted for features of another embodiment described herein. The description and drawings are accordingly to be regarded in an illustrative rather than a restrictive sense. 

What is claimed is:
 1. A system comprising: a brain interface system configured to be worn by a user and to output brain activity data representative of brain activity of the user while the user concurrently plays an electronic game; and a computing device configured to obtain the brain activity data, and modify, based on the brain activity data, an attribute of the electronic game; wherein the brain interface system comprises an optical measurement system configured to perform optical-based brain data acquisition operations, the brain activity data based on the optical-based brain data acquisition operations, the optical measurement system comprising: a wearable assembly configured to be worn by the user and comprising: a first slot surrounded by a first wall; a second slot surrounded by a second wall; a first module configured to be inserted into the first slot, the first module comprising a first light source configured to emit first light directed at a brain of the user and a first set of detectors configured to detect first arrival times for photons of the first light after the first light is scattered by the brain, the brain activity data based on the first arrival times; and a second module configured to be inserted into the second slot, the second module comprising a second light source configured to emit second light directed at the brain and a second set of detectors configured to detect second arrival times for photons of the second light after the second light is scattered by the brain, the brain activity data further based on the second arrival times; wherein a positioning of the first and second modules in the first and second slots is configured to cause a detector included in the first set of detectors of the first module to further detect third arrival times for photons of the second light after the second light is scattered by the brain, the brain activity further based on the third arrival times.
 2. The system of claim 1, wherein the positioning of the first and second modules in the first and second slots is further configured to cause a detector included in the second set of detectors of the second module to further detect fourth arrival times for photons of the first light after the first light is scattered by the brain, the brain activity further based on the fourth arrival times.
 3. The system of claim 1, wherein the first and second modules are configured to be interchangeably inserted into the first and second slots to acquire the brain activity data.
 4. The system of claim 3, wherein the detectors each comprise a plurality of single-photon avalanche diode (SPAD) circuits.
 5. The system of claim 1, wherein the brain interface system is further configured to perform electrical-based brain data acquisition operations, the brain activity data further based on the electrical-based brain data acquisition operations.
 6. The system of claim 5, wherein the brain interface system further comprises: a plurality of electrodes configured to be external to the user and detect electrical activity of the brain, the brain activity further based on the electrical activity.
 7. The system of claim 6, wherein the plurality of electrodes comprises a first electrode on a surface of the first module and a second electrode on a surface of the second module.
 8. The system of claim 7, wherein the first electrode surrounds the first light source on the surface of the first module.
 9. The system of claim 1, wherein the outputting the brain activity data, the obtaining the brain activity data, and the modifying the attribute of the electronic game are performed in substantially real time while the user concurrently plays the electronic game.
 10. The system of claim 1, wherein the electronic game requires physical interaction by the user with one or more user input devices for the user to play the electronic game.
 11. The system of claim 1, wherein: the computing device is further configured to obtain game data representative of one or more characteristics of the electronic game; and the modifying of the attribute of the electronic game is further based on the game data.
 12. The system of claim 11, wherein the one or more characteristics of the game represented by the game data comprise one or more of a current difficulty level of the electronic game, a current scene of the electronic game that the user is experiencing, a score achieved by the user while playing the electronic game, or an attribute of a user profile used by the user to play the electronic game.
 13. The system of claim 11, wherein the obtaining of the game data comprises receiving the game data from a gaming system that presents the electronic game to the user.
 14. The system of claim 1, wherein the computing device is further configured to present the electronic game to the user.
 15. The system of claim 1, wherein: the computing device is further configured to: determine, based on the brain activity data, a current mental state of the user, and obtain data representative of a desired mental state of the user; wherein the modifying the attribute of the electronic game comprises setting the attribute to a value configured to change the current mental state of the user to the desired mental state of the user.
 16. The system of claim 1, wherein the computing device is further configured to present, by way of a graphical user interface, content representative of the brain activity data.
 17. The system of claim 16, wherein the electronic game is presented to the user via the graphical user interface.
 18. The system of claim 16, wherein the graphical user interface is presented to one or more other users by way of a network.
 19. The system of claim 1, wherein: the computing device is further configured to obtain sensor data associated with the user from a sensor not included in the brain interface system; and the modifying of the attribute of the electronic game is further based on the sensor data.
 20. The system of claim 19, wherein the sensor data is representative of one or more of eye movement by the user, electrodermal activity of the user, pupillometry associated with the user, a heart rate of the user, heart rate variability of the user, or a pulse oximetry reading for the user.
 21. The system of claim 1, wherein the computing device is further configured to predict, based on the brain activity data, an action that the user will perform while playing the electronic game. 